“…A place where rivers, forests and savannas play a never-ending game with people. …”
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“…It also plays a key role in the biology and ecology of forest trees, affecting growth, water and nutrient absorption and protection against soil-borne pathogens. …”

“…In Africa, tree plantations are likely to be developed for several reasons: restoration of production capacities and services provided by natural forests, enhancement of agroforestry lands, easier harvesting of wood and non-wood forest products, etc. …”

Tabla de Contenidos: “…10.6.2 Data Flow Diagrams -- 10.6.2.1 Doctors -- 10.6.2.2 Patient -- 10.6.2.3 Transaction Flow -- 10.7 Performance Evaluation -- 10.7.1 Performance of the Proposed Model -- 10.7.2 Performance Comparison -- 10.8 Conclusions and Future Caveats -- References -- Chapter 11: AI-Aided Secured ECG Live Edge Monitoring System with a Practical Use-Case -- 11.1 Introduction -- 11.1.1 Background -- 11.1.2 Problem Statement -- 11.1.3 Objective and Scope -- 11.2 Related Work -- 11.3 Proposed AI-Based System Architecture -- 11.3.1 Block Diagram -- 11.3.2 Data Collection and Pre-Processing Steps -- 11.3.3 Detecting Heart Abnormalities Using AI-Aided Techniques -- 11.4 Considered Smart ECG Monitoring System -- 11.4.1 Edge Hardware Components -- 11.4.1.1 System-on-a-Chip (SoC) Model -- 11.4.1.2 IoT Sensor for Heart Rate Data Acquisition -- 11.4.1.3 Microprocessor and Analog to Digital Converter -- 11.4.2 AI-Logic Component -- 11.4.2.1 Decision Tree -- 11.4.2.2 Random Forest -- 11.4.2.3 ANN -- 11.4.2.4 CNN -- 11.5 Bio-Authentication Application of the Considered ECG Monitoring System for Specific Use-Cases -- 11.6 Performance Evaluation -- 11.6.1 Supraventricular Arrhythmia Classification -- 11.6.2 Authorized User Classification for Bio-Authentication System -- 11.7 Challenges Involved with the Proposed System -- Limitations -- 11.8 Conclusion and Future Scope -- References -- Section III -- Chapter 12: Application of Unmanned Aerial Vehicles in Wireless Networks: Mobile Edge Computing and Caching -- 12.1 Introduction -- 12.1.1 Chapter Roadmap -- 12.2 Literature Review -- 12.3 Description of Caching and Mobile Edge Computing -- 12.3.1 Overview of Caching -- 12.3.1.1 Advantages -- 12.3.1.2 Disadvantages -- 12.3.2 Overview of Mobile Edge Computing -- 12.3.2.1 Advantages -- 12.3.2.2 Disadvantages -- 12.4 Layering of UAV-Based MEC Architecture…”

Tabla de Contenidos: “…-- 3.1.2 Softmax and probability distributions -- 3.1.3 Interpreting the success of active learning -- 3.2 Algorithms for uncertainty sampling -- 3.2.1 Least confidence sampling -- 3.2.2 Margin of confidence sampling -- 3.2.3 Ratio sampling -- 3.2.4 Entropy (classification entropy) -- 3.2.5 A deep dive on entropy -- 3.3 Identifying when different types of models are confused -- 3.3.1 Uncertainty sampling with logistic regression and MaxEnt models -- 3.3.2 Uncertainty sampling with SVMs -- 3.3.3 Uncertainty sampling with Bayesian models -- 3.3.4 Uncertainty sampling with decision trees and random forests -- 3.4 Measuring uncertainty across multiple predictions -- 3.4.1 Uncertainty sampling with ensemble models -- 3.4.2 Query by Committee and dropouts -- 3.4.3 The difference between aleatoric and epistemic uncertainty -- 3.4.4 Multilabeled and continuous value classification -- 3.5 Selecting the right number of items for human review -- 3.5.1 Budget-constrained uncertainty sampling -- 3.5.2 Time-constrained uncertainty sampling -- 3.5.3 When do I stop if I'm not time- or budget-constrained? …”

Tabla de Contenidos: “…1.13.6 Applying Cognition to Develop Health and Wellness -- 1.13.7 Welltok -- 1.13.8 CaféWell Concierge in Action -- 1.14 Advantages of Cognitive Computing -- 1.15 Features of Cognitive Computing -- 1.16 Limitations of Cognitive Computing -- 1.17 Conclusion -- References -- 2 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges -- 2.1 Introduction -- 2.2 Cyber-Physical System Architecture -- 2.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS) -- 2.4 Machine Learning Applications in CPS -- 2.4.1 K-Nearest Neighbors (K-NN) in CPS -- 2.4.2 Support Vector Machine (SVM) in CPS -- 2.4.3 Random Forest (RF) in CPS -- 2.4.4 Decision Trees (DT) in CPS -- 2.4.5 Linear Regression (LR) in CPS -- 2.4.6 Multi-Layer Perceptron (MLP) in CPS -- 2.4.7 Naive Bayes (NB) in CPS -- 2.5 Use of IoT in CPS -- 2.6 Use of Big Data in CPS -- 2.7 Critical Analysis -- 2.8 Conclusion -- References -- 3 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Research Objectives -- 3.1.3 Research Approach -- 3.1.4 Limitations -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Methodological Approach -- 3.3.2 Methods of Analysis -- 3.4 Results -- 3.4.1 Impact of Project Outcomes -- 3.4.2 Results Obtained During the Methodology -- 3.5 Discussion -- 3.6 Originality and Innovativeness of the Research -- 3.6.1 Validation and Quality Control of Methods -- 3.6.2 Cost-Effectiveness of the Research -- 3.7 Conclusion -- References -- 4 Advanced Cognitive Models and Algorithms -- 4.1 Introduction -- 4.2 Microsoft Azure Cognitive Model -- 4.2.1 AI Services Broaden in Microsoft Azure -- 4.3 IBM Watson Cognitive Analytics -- 4.3.1 Cognitive Computing -- 4.3.2 Defining Cognitive Computing via IBM Watson Interface -- 4.3.3 IBM Watson Analytics -- 4.4 Natural Language Modeling…”

Tabla de Contenidos: “…3.4 Machine Learning Algorithms -- 3.4.1 Linear Regression -- 3.4.2 Logistic Regression -- 3.4.3 K-NN or K Nearest Neighbor -- 3.4.4 Decision Tree -- 3.4.5 Random Forest -- 3.5 Analysis and Prediction of COVID-19 Data -- 3.5.1 Methodology Adopted -- 3.6 Analysis Using Machine Learning Models -- 3.6.1 Splitting of Data into Training and Testing Data Set -- 3.6.2 Training of Machine Learning Models -- 3.6.3 Calculating the Score -- 3.7 Conclusion &amp -- Future Scope -- References -- 4 Rapid Forecasting of Pandemic Outbreak Using Machine Learning -- 4.1 Introduction -- 4.2 Effect of COVID-19 on Different Sections of Society -- 4.2.1 Effect of COVID-19 on Mental Health of Elder People -- 4.2.2 Effect of COVID-19 on our Environment -- 4.2.3 Effect of COVID-19 on International Allies and Healthcare -- 4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19 -- 4.2.5 Effect of COVID-19 on Labor Migrants -- 4.2.6 Impact of COVID-19 on our Economy -- 4.3 Definition and Types of Machine Learning -- 4.3.1 Machine Learning &amp -- Its Types -- 4.3.2 Applications of Machine Learning -- 4.4 Machine Learning Approaches for COVID-19 -- 4.4.1 Enabling Organizations to Regulate and Scale -- 4.4.2 Understanding About COVID-19 Infections -- 4.4.3 Gearing Up Study and Finding Treatments -- 4.4.4 Predicting Treatment and Healing Outcomes -- 4.4.5 Testing Patients and Diagnosing COVID-19 -- References -- 5 Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19 -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Suggested Methodology -- 5.4 Models in Epidemiology -- 5.4.1 Bayesian Inference Models -- 5.5 Particle Filtering Algorithm -- 5.6 MCM Model Implementation -- 5.6.1 Reproduction Number -- 5.7 Diagnosis of COVID-19 -- 5.7.1 Predicting Outbreaks Through Social Media Analysis -- 5.8 Conclusion -- References…”

Tabla de Contenidos: “…2.3.3.1 Ground object monitoring in the transmission line corridor -- 2.3.3.2 Geological disaster monitoring for the transmission line corridor -- 2.3.3.3 Monitoring the meteorological disasters of transmission lines corridors -- 2.3.3.4 Transmission line corridor forest fire monitoring -- 2.3.4 Wide Area Transmission Lines Monitoring Prospect Based on Satellite Remote Sensing Technology -- References -- 3 Tour inspection technology of transmission lines -- 3.1 Conventional Tour Inspection and its Classification -- 3.1.1 Regular Tour Inspection of Lines -- 3.1.2 Special tour inspection of lines -- 3.1.3 Fault Tour Inspection of Lines -- 3.1.4 On-the-Tower Tour Inspection of Lines -- 3.2 Main Contents of Tour Inspection of Lines -- 3.2.1 Environments Along the Lines -- 3.2.2 Towers, Guy Wires, and Foundations -- 3.2.3 Conductors and Ground Wires -- 3.2.4 Insulators and Fittings -- 3.2.5 Lightning Protection Facilities and Grounding Devices -- 3.2.6 Accessories and Other Facilities -- 3.3 Helicopter Tour Inspection Technology -- 3.3.1 Model of Line Inspection Helicopter -- 3.3.2 Line Inspection Heliborne Equipment -- 3.3.2.1 Infrared thermal imaging equipment -- 3.3.2.2 Ultraviolet (UV) imaging equipment -- 3.3.2.3 Heliborne laser radar -- 3.3.3 Tour Inspection Items -- 3.3.3.1 Regular inspection -- 3.3.3.2 Equipment fault detection -- 3.3.3.3 Vegetation management near transmission lines -- 3.3.3.4 The monitoring and management of the lines' operational state -- 3.3.3.5 Assessment of the electromagnetic environment of transmission lines -- 3.3.3.6 Equipment management of transmission lines -- 3.3.4 Helicopter Tour Inspection Process -- 3.3.4.1 Preparation for patrol -- 3.3.4.2 The patrol process and main defects found -- 3.4 Robot Tour Inspection Technology -- 3.4.1 Characteristics of Robot Tour Inspection…”

Tabla de Contenidos: “…4.4.2 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 4.4.3 Conclusion -- 4.5 Artificial Neural Networks -- 4.5.1 How It Works -- Step 1: Determine the Topology and Activation Function -- Step 2: Initiation -- Step 3: Calculating Error -- Step 4: Weight Adjustment -- 4.5.2 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 4.5.3 Conclusion -- 4.6 Support Vector Machines -- Concept and Terminology -- 4.6.1 How It Works -- 4.6.2 How to Implement -- Implementation 1: Linearly Separable Dataset -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Process Execution and Interpretation -- Example 2: Linearly Non-Separable Dataset -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Execution and Interpretation -- Parameter Settings -- 4.6.3 Conclusion -- 4.7 Ensemble Learners -- Wisdom of the Crowd -- 4.7.1 How It Works -- Achieving the Conditions for Ensemble Modeling -- 4.7.2 How to Implement -- Ensemble by Voting -- Bootstrap Aggregating or Bagging -- Implementation -- Boosting -- AdaBoost -- Implementation -- Random Forest -- Implementation -- 4.7.3 Conclusion -- References -- 5 Regression Methods -- 5.1 Linear Regression -- 5.1.1 How it Works -- 5.1.2 How to Implement -- Step 1: Data Preparation -- Step 2: Model Building -- Step 3: Execution and Interpretation -- Step 4: Application to Unseen Test Data -- 5.1.3 Checkpoints -- 5.2 Logistic Regression -- 5.2.1 How It Works -- How Does Logistic Regression Find the Sigmoid Curve? …”

Tabla de Contenidos: “…Chapter 10 Research Design Machine Maintenance Management Software Module for Garment Industry -- 10.1 Introduction -- 10.2 Building a Maintenance Process for Garment Industry Machine -- 10.2.1 Maintenance Process for Machinery -- 10.2.2 Information in the Maintenance Management Machine Records -- 10.3 Designing a "Machine Maintenance Management" Software Module -- 10.3.1 Database Design -- 10.3.2 Designing a "Machine Maintenance Management" Software Module -- 10.4 Conclusion -- References -- Part 3: Adoption of ICT for Digitalization, Artificial Intelligence, and Machine Learning -- Chapter 11 Performance Comparison of Prediction of a Hydraulic Jump Depth in a Channel Using Various Machine Learning Models -- Nomenclature -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Materials and Methods -- 11.3.1 Equation of the Hydraulic Jump -- 11.3.2 Data Used in the Study -- 11.4 Machine Learning Models -- 11.4.1 Features of Machine Learning Models -- 11.4.2 Support Vector Machine (SVM) -- 11.4.3 Decision Tree (DT) -- 11.4.4 Random Forest (RF) -- 11.4.5 Artificial Neural Network (ANN) -- 11.5 Results and Discussion -- 11.6 Conclusions -- References -- Chapter 12 Creating a Video from Facial Image Using Conditional Generative Adversarial Network -- 12.1 Introduction -- 12.2 Related Works -- 12.3 Methodology -- 12.3.1 The Proposed Model -- 12.3.2 Conditional Generative Adversarial Network (cGAN) -- 12.3.3 Hidden Affine Transformation -- 12.4 Experiments -- 12.4.1 Dataset -- 12.4.2 Dlib -- 12.4.3 Evaluation -- 12.4.4 Result -- 12.5 Conclusion -- References -- Chapter 13 Deep Learning Framework for Detecting, Classifying, and Recognizing Invoice Metadata -- 13.1 Introduction -- 13.2 Related Works -- 13.3 Invoice Data Analysis -- 13.4 Proposed Method -- 13.5 Experiments -- 13.6 Conclusion and Perspectives -- References…”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Elevating Surveillance Integrity-Mathematical Insights into Background Subtraction in Image Processing -- 1.1 Introduction -- 1.2 Background Subtraction -- 1.3 Mathematics Behind Background Subtraction -- 1.4 Gaussian Mixture Model -- 1.4.1 Gaussian Mixture Model (GMM) Algorithm for Background Subtraction -- 1.4.2 Gaussian Mixture Model (GMM) Algorithm - A Simple Example -- 1.5 Principal Component Analysis -- 1.6 Applications -- 1.6.1 Military Surveillance -- 1.6.2 Visual Observation of Animals in Forests -- 1.6.3 Marine Surveillance -- 1.6.4 Defense Surveillance Systems -- 1.7 Conclusion -- References -- Chapter 2 Machine Learning and Artificial Intelligence in the Detection of Moving Objects Using Image Processing -- 2.1 Introduction -- 2.2 Moving Object Detection -- 2.3 Envisaging the Object Detection -- 2.3.1 Filtering Algorithm -- 2.3.2 Identification of Object Detection in Bad Weather Circumstance -- 2.3.3 Color Clustering -- 2.3.4 Dangerous Animal Detection -- 2.3.5 UAV Video End-of-Line Detection and Tracking in Live Traffic -- 2.3.5.1 Contextual Detection -- 2.3.5.2 Calculation of Location of a Car -- 2.3.6 Estimation of Crowd -- 2.3.7 Parking Lot Management -- 2.3.8 Public Automatic Anomaly Detection Systems -- 2.3.9 Modification of Robust Principal Component Analysis -- 2.3.10 Logistics Automation -- 2.3.11 Detection of Criminal Behavior in Humans -- 2.3.12 UAV Collision Avoidance and Control System -- 2.3.13 An Overview of Potato Growth Stages -- 2.4 Conclusion -- References -- Chapter 3 Machine Learning and Imaging-Based Vehicle Classification for Traffic Monitoring Systems -- 3.1 Introduction -- 3.2 Methods -- 3.2.1 Data Preparation -- 3.2.2 Model Training -- 3.2.3 Hardware and Software Configuration -- 3.3 Result -- 3.4 Conclusion…”

Tabla de Contenidos: “…8.2 Methodology -- 8.3 AI-Based Predictive Modeling -- 8.3.1 Linear Regression -- 8.3.2 Random Forests -- 8.3.3 XGBoost -- 8.3.4 SVM -- 8.4 Performance Indices -- 8.4.1 Root Mean Squared Error (RMSE) -- 8.4.2 Mean Squared Error (MSE) -- 8.4.3 R2 (R-Squared) -- 8.5 Results and Discussion -- 8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase -- 8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase -- 8.5.3 K Cross Fold Validation -- 8.6 Conclusions -- References -- Chapter 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Research Methodology -- 9.3.1 Dataset and Metrics -- 9.4 Result and Discussion -- 9.5 Conclusion -- References -- Chapter 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA -- 10.1 Introduction -- 10.2 System Model -- 10.3 User Clustering -- 10.4 Optimal Power Allocation for EE-SE Tradeoff -- 10.4.1 Multiobjective Optimization Problem -- 10.4.2 Multiobjective PSO -- 10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA -- 10.5 Numerical Results -- 10.6 Conclusion -- References -- Chapter 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews -- 11.1 Introduction -- 11.1.1 Related Work -- 11.2 Materials and Methods -- 11.2.1 Data Cleaning and Pre-Processing -- 11.2.2 Feature Extraction -- 11.2.3 Classifiers -- 11.3 Results and Experiments -- 11.4 Conclusion -- References -- Chapter 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm -- 12.1 Introduction -- 12.2 Genetic Algorithm GA: An Evolutionary Computational Technique -- 12.3 Design of Multiobjective Optimization Problem…”